Set Options

knitr::opts_chunk$set(
  warning = TRUE, # show warnings during codebook generation
  message = TRUE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
                # usually better for debugging
  echo = TRUE  # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())

library(rio)
library(labelled)

Prep Data

library(codebook)
## 
## Attaching package: 'codebook'
## The following object is masked from 'package:labelled':
## 
##     to_factor
codebook_data <- import("output_data/ko_full_data.csv.gz")
## data.table::fread() does not support reading from connections. Using utils::read.table() instead.
var_label(codebook_data) <- list(
        observation = "Unique participant ID number.",
        sender = "Section of the experiment currently displayed.",
        sender_type = "Type of labjs object currently displayed.",
        sender_id = "Order of blocks shown from labjs. Underscores separate different components to the block (block_task_trial_item).",
        response = "Participant response to the trial.",
        response_action = "Keypress used to indicate their response to the trial.", 
        ended_on = "How the trial ended (timeout, form submit, completion, response).",
        duration = "The duration in milliseconds of the entire trial from time shown to time end.",
        time_run = "The time in milliseconds from the start of the experiment it took to run (start to display) the trial.",
        time_render = "The time in milliseconds from the start of the experiment it took to render (prepare, get ready for) the trial.",
        time_show = "The time in milliseconds from the start of the experiment it took to show the trial on the screen to the participant.",
        time_end = "The time in milliseconds from the start of the experiment it took to end the current trial.",
        time_commit = "The time in milliseconds from the start of the experiment it took to save the current trial.",
        timestamp = "The approximate timestamp of the trial in UTC server time.",
        time_switch = "The time in milliseconds from the start of the experiment it took to switch between the previous trial and the current trial.",
        url_lab = "The lab code for the PSA member that ran the study.",
        meta_labjs_version = "The version of labjs used in the study.",
        meta_user_agent = "The browser the participant used in the study.",
        meta_platform = "The operating system of the computer used in the experiment.",
        meta_language = "The default language set for the browser the participant used in the study.",
        meta_locale = "The location of the browser the participant used in the study.",
        meta_time_zone = "The timezone set for the browser/computer the participant used in the study.",
        meta_timezone_offset = "The time zone offset from UTC time.",
        meta_screen_width = "The width of the screen in pixels.",
        meta_screen_height = "The height of the screen in pixels.",
        meta_scroll_height = "The height of the scroll bar.",
        meta_scroll_width = "The width of the scroll bar.", 
        meta_window_inner_width = "The width of the browser window in pixels.",
        meta_window_inner_height = "The height of the browser window in pixels.",
        meta_device_pixel_ratio = "The ratio of width to height of the screen in pixels.",
        meta_labjs_build_flavor = "The version build of the labjs version - usually production.",
        meta_labjs_build_commit = "The commit version of the labjs build.",
        please_tell_us_your_gender = "A multiple choice option for the gender of the participant. All answer choices were in the target language, but are presented in English equivalents here.",
        which_year_were_you_born = "A numeric entry box for the year of birth for the participant.",
        please_tell_us_your_education_level = "A multiple choice option for the education level of the participant. All answer choices were in the target language, but are presented in English equivalents here.",
        native_language = "An open choice answer box for the native language of the participant.",
        dominanthand = "The domininant hand indicated by the participant, which controlled the keys pressed for each answer choice (word or nonword).",
        word = "The string of letters/characters shown on the screen for the trial.",
        class = "The type of stimuli shown on the screen (word or nonword).",
        correct_response = "The correct answer for the trial.",
        correct = "A logical variable indicating if the participant got the trial answer correct.",
        feedback = "The feedback a participant received during practice trials.",
        fix_sender = "The sender_id column in a sortable format. You can sort the data by observation and this column to ensure it is in trial order."
)

metadata(codebook_data)$name <- "Semantic Priming Across Many Languages Korean"
metadata(codebook_data)$description <- "This dataset contains the raw trial data of the Korean data collection from the SPAML project with funding from ZPID and a Bilendi data collection team. The data is presented here in long format, with each trial representing one row in the data. Please note that the information about the build of the study will only display on the first trial, and the demographic information will only display on the trial that collected this information. You can assume all other rows with the same observation ID are those same build and demographics. Other 'missing' data occurs when a column is not relevant for that trial (i.e., correct will not show for non-word trial pages). 

Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. These studies provide insight into the cognitive underpinnings of semantic representations in both healthy and clinical populations; however, they have suffered from several issues including generally low sample sizes and a lack of diversity in linguistic implementations. Here, we will test the size and the variability of the semantic priming effect across ten languages by creating a large database of semantic priming values, based on an adaptive sampling procedure. Differences in response latencies between related word-pair conditions and unrelated word-pair conditions (i.e., difference score confidence interval is greater than zero) will allow quantifying evidence for semantic priming, whereas improvements in model fit with the addition of a random intercept for language will provide support for variability in semantic priming across languages."
metadata(codebook_data)$identifier <- "https://doi.org/10.23668/psycharchives.7074"
metadata(codebook_data)$creator <- "Erin M. Buchanan"
metadata(codebook_data)$citation <- "Buchanan, E. M., Cuccolo, K., Coles, N. A., Heyman, T., Iyer, A., Lewis, N. A., Jr., … Lewis, S. C. (2021, December 7). Measuring the Semantic Priming Effect Across Many Languages. https://doi.org/10.31219/osf.io/q4fjy"
metadata(codebook_data)$url <- "https://osf.io/wrpj4/"
metadata(codebook_data)$datePublished <- "2023-02-10"
metadata(codebook_data)$temporalCoverage <- "2022-2023" 
metadata(codebook_data)$spatialCoverage <- "Online" 

Create codebook

codebook(codebook_data)

Metadata

Description

Dataset name: Semantic Priming Across Many Languages Korean

This dataset contains the raw trial data of the Korean data collection from the SPAML project with funding from ZPID and a Bilendi data collection team. The data is presented here in long format, with each trial representing one row in the data. Please note that the information about the build of the study will only display on the first trial, and the demographic information will only display on the trial that collected this information. You can assume all other rows with the same observation ID are those same build and demographics. Other ‘missing’ data occurs when a column is not relevant for that trial (i.e., correct will not show for non-word trial pages).

Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. These studies provide insight into the cognitive underpinnings of semantic representations in both healthy and clinical populations; however, they have suffered from several issues including generally low sample sizes and a lack of diversity in linguistic implementations. Here, we will test the size and the variability of the semantic priming effect across ten languages by creating a large database of semantic priming values, based on an adaptive sampling procedure. Differences in response latencies between related word-pair conditions and unrelated word-pair conditions (i.e., difference score confidence interval is greater than zero) will allow quantifying evidence for semantic priming, whereas improvements in model fit with the addition of a random intercept for language will provide support for variability in semantic priming across languages.

Metadata for search engines
name value
1 Erin M. Buchanan
x
observation
sender
sender_type
sender_id
response
response_action
ended_on
duration
time_run
time_render
time_show
time_end
time_commit
timestamp
time_switch
url_lab
meta_labjs_version
meta_user_agent
meta_platform
meta_language
meta_locale
meta_time_zone
meta_timezone_offset
meta_screen_width
meta_screen_height
meta_scroll_width
meta_scroll_height
meta_window_inner_width
meta_window_inner_height
meta_device_pixel_ratio
meta_labjs_build_flavor
meta_labjs_build_commit
please_tell_us_your_gender
which_year_were_you_born
please_tell_us_your_education_level
native_language
dominanthand
word
class
feedback
correct_response
correct
fix_sender

#Variables

observation

Unique participant ID number.

Distribution

Distribution of values for observation

Distribution of values for observation

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
observation Unique participant ID number. character 0 1 1575 0 14 14 0

sender

Section of the experiment currently displayed.

Distribution

Distribution of values for sender

Distribution of values for sender

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
sender Section of the experiment currently displayed. character 0 1 23 0 5 23 0

sender_type

Type of labjs object currently displayed.

Distribution

Distribution of values for sender_type

Distribution of values for sender_type

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
sender_type Type of labjs object currently displayed. character 0 1 6 0 9 13 0

sender_id

Order of blocks shown from labjs. Underscores separate different components to the block (block_task_trial_item).

Distribution

Distribution of values for sender_id

Distribution of values for sender_id

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
sender_id Order of blocks shown from labjs. Underscores separate different components to the block (block_task_trial_item). character 0 1 2475 0 1 10 0

response

Participant response to the trial.

Distribution

Distribution of values for response

Distribution of values for response

1653059 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
response Participant response to the trial. character 1653059 0.3162411 3 0 4 8 0

response_action

Keypress used to indicate their response to the trial.

Distribution

Distribution of values for response_action

Distribution of values for response_action

1653059 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
response_action Keypress used to indicate their response to the trial. character 1653059 0.3162411 3 0 11 15 0

ended_on

How the trial ended (timeout, form submit, completion, response).

Distribution

Distribution of values for ended_on

Distribution of values for ended_on

8960 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
ended_on How the trial ended (timeout, form submit, completion, response). character 8960 0.9962939 4 0 7 15 0

duration

The duration in milliseconds of the entire trial from time shown to time end.

Distribution

Distribution of values for duration

Distribution of values for duration

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
duration The duration in milliseconds of the entire trial from time shown to time end. numeric 0 1 -655 616 2.1e+07 2070.501 42989.67 ▇▁▁▁▁

time_run

The time in milliseconds from the start of the experiment it took to run (start to display) the trial.

Distribution

Distribution of values for time_run

Distribution of values for time_run

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
time_run The time in milliseconds from the start of the experiment it took to run (start to display) the trial. numeric 0 1 37 593754 2.9e+07 821313.5 1428197 ▇▁▁▁▁

time_render

The time in milliseconds from the start of the experiment it took to render (prepare, get ready for) the trial.

Distribution

Distribution of values for time_render

Distribution of values for time_render

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
time_render The time in milliseconds from the start of the experiment it took to render (prepare, get ready for) the trial. numeric 0 1 44 593751 2.9e+07 821312.4 1428197 ▇▁▁▁▁

time_show

The time in milliseconds from the start of the experiment it took to show the trial on the screen to the participant.

Distribution

Distribution of values for time_show

Distribution of values for time_show

3665 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
time_show The time in milliseconds from the start of the experiment it took to show the trial on the screen to the participant. numeric 3665 0.998484 65 593795 2.9e+07 821798.9 1430169 ▇▁▁▁▁

time_end

The time in milliseconds from the start of the experiment it took to end the current trial.

Distribution

Distribution of values for time_end

Distribution of values for time_end

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
time_end The time in milliseconds from the start of the experiment it took to end the current trial. numeric 0 1 478 6e+05 2.9e+07 823590.5 1429791 ▇▁▁▁▁

time_commit

The time in milliseconds from the start of the experiment it took to save the current trial.

Distribution

Distribution of values for time_commit

Distribution of values for time_commit

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
time_commit The time in milliseconds from the start of the experiment it took to save the current trial. numeric 0 1 481 6e+05 2.9e+07 823592.4 1429791 ▇▁▁▁▁

timestamp

The approximate timestamp of the trial in UTC server time.

Distribution

Distribution of values for timestamp

Distribution of values for timestamp

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
timestamp The approximate timestamp of the trial in UTC server time. character 0 1 263362 0 19 19 0

time_switch

The time in milliseconds from the start of the experiment it took to switch between the previous trial and the current trial.

Distribution

Distribution of values for time_switch

Distribution of values for time_switch

1948 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
time_switch The time in milliseconds from the start of the experiment it took to switch between the previous trial and the current trial. numeric 1948 0.9991942 487 6e+05 2.9e+07 823564.2 1430253 ▇▁▁▁▁

url_lab

The lab code for the PSA member that ran the study.

Distribution

Distribution of values for url_lab

Distribution of values for url_lab

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
url_lab The lab code for the PSA member that ran the study. character 2416030 0.0006515 3 0 2 4 0

meta_labjs_version

The version of labjs used in the study.

Distribution

Distribution of values for meta_labjs_version

Distribution of values for meta_labjs_version

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
meta_labjs_version The version of labjs used in the study. character 2416030 0.0006515 1 0 6 6 0

meta_user_agent

The browser the participant used in the study.

Distribution

Distribution of values for meta_user_agent

Distribution of values for meta_user_agent

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
meta_user_agent The browser the participant used in the study. character 2416030 0.0006515 93 0 79 156 0

meta_platform

The operating system of the computer used in the experiment.

Distribution

Distribution of values for meta_platform

Distribution of values for meta_platform

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
meta_platform The operating system of the computer used in the experiment. character 2416030 0.0006515 3 0 5 12 0

meta_language

The default language set for the browser the participant used in the study.

Distribution

Distribution of values for meta_language

Distribution of values for meta_language

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
meta_language The default language set for the browser the participant used in the study. character 2416030 0.0006515 8 0 2 5 0

meta_locale

The location of the browser the participant used in the study.

Distribution

Distribution of values for meta_locale

Distribution of values for meta_locale

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
meta_locale The location of the browser the participant used in the study. character 2416030 0.0006515 7 0 2 5 0

meta_time_zone

The timezone set for the browser/computer the participant used in the study.

Distribution

Distribution of values for meta_time_zone

Distribution of values for meta_time_zone

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
meta_time_zone The timezone set for the browser/computer the participant used in the study. character 2416030 0.0006515 25 0 9 20 0

meta_timezone_offset

The time zone offset from UTC time.

Distribution

Distribution of values for meta_timezone_offset

Distribution of values for meta_timezone_offset

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
meta_timezone_offset The time zone offset from UTC time. numeric 2416030 0.0006515 -780 -540 600 -506.6667 164.3612 ▇▁▁▁▁

meta_screen_width

The width of the screen in pixels.

Distribution

Distribution of values for meta_screen_width

Distribution of values for meta_screen_width

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
meta_screen_width The width of the screen in pixels. numeric 2416030 0.0006515 820 1920 3840 1774.714 353.6158 ▂▇▁▁▁

meta_screen_height

The height of the screen in pixels.

Distribution

Distribution of values for meta_screen_height

Distribution of values for meta_screen_height

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
meta_screen_height The height of the screen in pixels. numeric 2416030 0.0006515 512 1080 2160 1004.295 164.593 ▁▇▁▁▁

meta_scroll_width

The width of the scroll bar.

Distribution

Distribution of values for meta_scroll_width

Distribution of values for meta_scroll_width

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
meta_scroll_width The width of the scroll bar. numeric 2416030 0.0006515 500 1587 3072 1620.992 321.9303 ▁▅▇▁▁

meta_scroll_height

The height of the scroll bar.

Distribution

Distribution of values for meta_scroll_height

Distribution of values for meta_scroll_height

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
meta_scroll_height The height of the scroll bar. numeric 2416030 0.0006515 322 864 1538 804.7498 152.6335 ▁▆▇▁▁

meta_window_inner_width

The width of the browser window in pixels.

Distribution

Distribution of values for meta_window_inner_width

Distribution of values for meta_window_inner_width

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
meta_window_inner_width The width of the browser window in pixels. numeric 2416030 0.0006515 500 1587 3072 1620.988 321.9305 ▁▅▇▁▁

meta_window_inner_height

The height of the browser window in pixels.

Distribution

Distribution of values for meta_window_inner_height

Distribution of values for meta_window_inner_height

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
meta_window_inner_height The height of the browser window in pixels. numeric 2416030 0.0006515 322 912 1586 850.9917 155.6559 ▁▆▇▁▁

meta_device_pixel_ratio

The ratio of width to height of the screen in pixels.

Distribution

Distribution of values for meta_device_pixel_ratio

Distribution of values for meta_device_pixel_ratio

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
meta_device_pixel_ratio The ratio of width to height of the screen in pixels. numeric 2416030 0.0006515 0.9 1 3 1.110367 0.2318844 ▇▁▁▁▁

meta_labjs_build_flavor

The version build of the labjs version - usually production.

Distribution

Distribution of values for meta_labjs_build_flavor

Distribution of values for meta_labjs_build_flavor

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
meta_labjs_build_flavor The version build of the labjs version - usually production. character 2416030 0.0006515 1 0 10 10 0

meta_labjs_build_commit

The commit version of the labjs build.

Distribution

Distribution of values for meta_labjs_build_commit

Distribution of values for meta_labjs_build_commit

2416030 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
meta_labjs_build_commit The commit version of the labjs build. character 2416030 0.0006515 1 0 40 40 0

please_tell_us_your_gender

A multiple choice option for the gender of the participant. All answer choices were in the target language, but are presented in English equivalents here.

Distribution

Distribution of values for please_tell_us_your_gender

Distribution of values for please_tell_us_your_gender

2416153 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
please_tell_us_your_gender A multiple choice option for the gender of the participant. All answer choices were in the target language, but are presented in English equivalents here. character 2416153 0.0006006 4 0 4 6 0

which_year_were_you_born

A numeric entry box for the year of birth for the participant.

Distribution

Distribution of values for which_year_were_you_born

Distribution of values for which_year_were_you_born

2416147 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
which_year_were_you_born A numeric entry box for the year of birth for the participant. numeric 2416147 0.0006031 1920 1978 2008 1977.64 11.69558 ▁▁▅▇▂

please_tell_us_your_education_level

A multiple choice option for the education level of the participant. All answer choices were in the target language, but are presented in English equivalents here.

Distribution

Distribution of values for please_tell_us_your_education_level

Distribution of values for please_tell_us_your_education_level

2416153 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
please_tell_us_your_education_level A multiple choice option for the education level of the participant. All answer choices were in the target language, but are presented in English equivalents here. character 2416153 0.0006006 5 0 6 21 0

native_language

An open choice answer box for the native language of the participant.

Distribution

Distribution of values for native_language

Distribution of values for native_language

2416228 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
native_language An open choice answer box for the native language of the participant. character 2416228 0.0005696 26 0 1 18 0

dominanthand

The domininant hand indicated by the participant, which controlled the keys pressed for each answer choice (word or nonword).

Distribution

Distribution of values for dominanthand

Distribution of values for dominanthand

2416147 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
dominanthand The domininant hand indicated by the participant, which controlled the keys pressed for each answer choice (word or nonword). character 2416147 0.0006031 2 0 4 5 0

word

The string of letters/characters shown on the screen for the trial.

Distribution

Distribution of values for word

Distribution of values for word

30335 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
word The string of letters/characters shown on the screen for the trial. character 30335 0.9874525 3803 0 1 13 0

class

The type of stimuli shown on the screen (word or nonword).

Distribution

Distribution of values for class

Distribution of values for class

30335 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
class The type of stimuli shown on the screen (word or nonword). character 30335 0.9874525 2 0 4 7 0

feedback

The feedback a participant received during practice trials.

Distribution

Distribution of values for feedback

Distribution of values for feedback

2404642 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
feedback The feedback a participant received during practice trials. character 2404642 0.0053619 3 0 3 13 0

correct_response

The correct answer for the trial.

Distribution

Distribution of values for correct_response

Distribution of values for correct_response

1663238 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
correct_response The correct answer for the trial. character 1663238 0.3120307 2 0 4 7 0

correct

A logical variable indicating if the participant got the trial answer correct.

Distribution

Distribution of values for correct

Distribution of values for correct

1663238 missing values.

Summary statistics

name label data_type n_missing complete_rate count mean
correct A logical variable indicating if the participant got the trial answer correct. logical 1663238 0.3120307 TRU: 671354, FAL: 83013 0.8899567

fix_sender

The sender_id column in a sortable format. You can sort the data by observation and this column to ensure it is in trial order.

Distribution

Distribution of values for fix_sender

Distribution of values for fix_sender

1639259 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
fix_sender The sender_id column in a sortable format. You can sort the data by observation and this column to ensure it is in trial order. character 1639259 0.3219492 802 0 11 11 0

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "Semantic Priming Across Many Languages Korean",
  "description": "This dataset contains the raw trial data of the Korean data collection from the SPAML project with funding from ZPID and a Bilendi data collection team. The data is presented here in long format, with each trial representing one row in the data. Please note that the information about the build of the study will only display on the first trial, and the demographic information will only display on the trial that collected this information. You can assume all other rows with the same observation ID are those same build and demographics. Other 'missing' data occurs when a column is not relevant for that trial (i.e., correct will not show for non-word trial pages). \n\nSemantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. These studies provide insight into the cognitive underpinnings of semantic representations in both healthy and clinical populations; however, they have suffered from several issues including generally low sample sizes and a lack of diversity in linguistic implementations. Here, we will test the size and the variability of the semantic priming effect across ten languages by creating a large database of semantic priming values, based on an adaptive sampling procedure. Differences in response latencies between related word-pair conditions and unrelated word-pair conditions (i.e., difference score confidence interval is greater than zero) will allow quantifying evidence for semantic priming, whereas improvements in model fit with the addition of a random intercept for language will provide support for variability in semantic priming across languages.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "identifier": "https://doi.org/10.23668/psycharchives.7074",
  "creator": "Erin M. Buchanan",
  "citation": "Buchanan, E. M., Cuccolo, K., Coles, N. A., Heyman, T., Iyer, A., Lewis, N. A., Jr., … Lewis, S. C. (2021, December 7). Measuring the Semantic Priming Effect Across Many Languages. https://doi.org/10.31219/osf.io/q4fjy",
  "url": "https://osf.io/wrpj4/",
  "datePublished": "2023-02-10",
  "temporalCoverage": "2022-2023",
  "spatialCoverage": "Online",
  "keywords": ["observation", "sender", "sender_type", "sender_id", "response", "response_action", "ended_on", "duration", "time_run", "time_render", "time_show", "time_end", "time_commit", "timestamp", "time_switch", "url_lab", "meta_labjs_version", "meta_user_agent", "meta_platform", "meta_language", "meta_locale", "meta_time_zone", "meta_timezone_offset", "meta_screen_width", "meta_screen_height", "meta_scroll_width", "meta_scroll_height", "meta_window_inner_width", "meta_window_inner_height", "meta_device_pixel_ratio", "meta_labjs_build_flavor", "meta_labjs_build_commit", "please_tell_us_your_gender", "which_year_were_you_born", "please_tell_us_your_education_level", "native_language", "dominanthand", "word", "class", "feedback", "correct_response", "correct", "fix_sender"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "observation",
      "description": "Unique participant ID number.",
      "@type": "propertyValue"
    },
    {
      "name": "sender",
      "description": "Section of the experiment currently displayed.",
      "@type": "propertyValue"
    },
    {
      "name": "sender_type",
      "description": "Type of labjs object currently displayed.",
      "@type": "propertyValue"
    },
    {
      "name": "sender_id",
      "description": "Order of blocks shown from labjs. Underscores separate different components to the block (block_task_trial_item).",
      "@type": "propertyValue"
    },
    {
      "name": "response",
      "description": "Participant response to the trial.",
      "@type": "propertyValue"
    },
    {
      "name": "response_action",
      "description": "Keypress used to indicate their response to the trial.",
      "@type": "propertyValue"
    },
    {
      "name": "ended_on",
      "description": "How the trial ended (timeout, form submit, completion, response).",
      "@type": "propertyValue"
    },
    {
      "name": "duration",
      "description": "The duration in milliseconds of the entire trial from time shown to time end.",
      "@type": "propertyValue"
    },
    {
      "name": "time_run",
      "description": "The time in milliseconds from the start of the experiment it took to run (start to display) the trial.",
      "@type": "propertyValue"
    },
    {
      "name": "time_render",
      "description": "The time in milliseconds from the start of the experiment it took to render (prepare, get ready for) the trial.",
      "@type": "propertyValue"
    },
    {
      "name": "time_show",
      "description": "The time in milliseconds from the start of the experiment it took to show the trial on the screen to the participant.",
      "@type": "propertyValue"
    },
    {
      "name": "time_end",
      "description": "The time in milliseconds from the start of the experiment it took to end the current trial.",
      "@type": "propertyValue"
    },
    {
      "name": "time_commit",
      "description": "The time in milliseconds from the start of the experiment it took to save the current trial.",
      "@type": "propertyValue"
    },
    {
      "name": "timestamp",
      "description": "The approximate timestamp of the trial in UTC server time.",
      "@type": "propertyValue"
    },
    {
      "name": "time_switch",
      "description": "The time in milliseconds from the start of the experiment it took to switch between the previous trial and the current trial.",
      "@type": "propertyValue"
    },
    {
      "name": "url_lab",
      "description": "The lab code for the PSA member that ran the study.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_labjs_version",
      "description": "The version of labjs used in the study.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_user_agent",
      "description": "The browser the participant used in the study.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_platform",
      "description": "The operating system of the computer used in the experiment.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_language",
      "description": "The default language set for the browser the participant used in the study.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_locale",
      "description": "The location of the browser the participant used in the study.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_time_zone",
      "description": "The timezone set for the browser/computer the participant used in the study.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_timezone_offset",
      "description": "The time zone offset from UTC time.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_screen_width",
      "description": "The width of the screen in pixels.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_screen_height",
      "description": "The height of the screen in pixels.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_scroll_width",
      "description": "The width of the scroll bar.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_scroll_height",
      "description": "The height of the scroll bar.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_window_inner_width",
      "description": "The width of the browser window in pixels.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_window_inner_height",
      "description": "The height of the browser window in pixels.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_device_pixel_ratio",
      "description": "The ratio of width to height of the screen in pixels.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_labjs_build_flavor",
      "description": "The version build of the labjs version - usually production.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_labjs_build_commit",
      "description": "The commit version of the labjs build.",
      "@type": "propertyValue"
    },
    {
      "name": "please_tell_us_your_gender",
      "description": "A multiple choice option for the gender of the participant. All answer choices were in the target language, but are presented in English equivalents here.",
      "@type": "propertyValue"
    },
    {
      "name": "which_year_were_you_born",
      "description": "A numeric entry box for the year of birth for the participant.",
      "@type": "propertyValue"
    },
    {
      "name": "please_tell_us_your_education_level",
      "description": "A multiple choice option for the education level of the participant. All answer choices were in the target language, but are presented in English equivalents here.",
      "@type": "propertyValue"
    },
    {
      "name": "native_language",
      "description": "An open choice answer box for the native language of the participant.",
      "@type": "propertyValue"
    },
    {
      "name": "dominanthand",
      "description": "The domininant hand indicated by the participant, which controlled the keys pressed for each answer choice (word or nonword).",
      "@type": "propertyValue"
    },
    {
      "name": "word",
      "description": "The string of letters/characters shown on the screen for the trial.",
      "@type": "propertyValue"
    },
    {
      "name": "class",
      "description": "The type of stimuli shown on the screen (word or nonword).",
      "@type": "propertyValue"
    },
    {
      "name": "feedback",
      "description": "The feedback a participant received during practice trials.",
      "@type": "propertyValue"
    },
    {
      "name": "correct_response",
      "description": "The correct answer for the trial.",
      "@type": "propertyValue"
    },
    {
      "name": "correct",
      "description": "A logical variable indicating if the participant got the trial answer correct.",
      "@type": "propertyValue"
    },
    {
      "name": "fix_sender",
      "description": "The sender_id column in a sortable format. You can sort the data by observation and this column to ensure it is in trial order.",
      "@type": "propertyValue"
    }
  ]
}`